Organizations with a large number of computers that run several different services typically monitor both hardware and software events for anomalies that can indicate security threats. To date, operational security assurance procedures are typically based on rules that analyze events for pre-defined patterns. For example, the rules may be run against logs of each computer. The pre-defined patterns can indicate a potential security threat which, once identified, can be addressed. This rule-based approach can fail to scale in at least two dimensions, thus rendering the approach difficult to flexibly implement. First, regarding rule-based implementations, such requires coding of the rules ahead of time, based on expert knowledge. This means that rule developers have to anticipate what is sought as a vulnerability. Rule developers may not, however, be knowledgeable of all potential vulnerabilities, thus leaving gaps in the approach. Second, during operation, the rule-based approach demands full scanning of all events, seeking for patterns in data or information that may have incomplete or incorrect data.
Thus, it can be difficult to achieve good or satisfactory results because such systems can typically fail to either recognize important security events, or can produce too many false positives, thus triggering unnecessary investigations.